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Novel Metrics for Object Discrimination in Overhead Real and Synthetic Color Images

机译：头顶实色和合成彩色图像中目标识别的新指标

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摘要

The evaluation and discrimination of similar objects in real versus synthetically generated aerial color images is needed for security and surveillance purposes among other applications. Identification of appropriate discrimination metrics between real versus synthetic images may also help in more robust generation of these synthetic images. In this paper, we investigate the effectiveness of three different metrics based on Gaussian Blur, Differential Operators and singular value decomposition (SVD) to differentiate between a pair of same objects contained in real and synthetic overhead aerial color images. We use nine pairs of images in our tests. The real images were obtained in the visible aerial color image domain. The proposed metrics are used to discriminate between pairs of real and synthetic objects such as cooling units, industrial buildings, houses, conveyors, stacks, piles, railroads and ponds in these real and synthetically generated images respectively. The proposed method successfully discriminates between the real and synthetic objects in aerial color images without any apriori knowledge or extra information such as optical flow. We ranked these metrics according to their effectiveness to discriminate between synthetic and real objects in overhead images.
机译：在其他应用中，出于安全和监视目的，需要评估和区分真实物体与合成生成的航空彩色图像中的相似物体。真实图像与合成图像之间的适当区分度的标识也可以帮助更可靠地生成这些合成图像。在本文中，我们研究了基于高斯模糊，微分算子和奇异值分解（SVD）的三种不同度量来区分实际和合成架空航空彩色图像中包含的一对相同对象的有效性。我们在测试中使用了九对图像。真实图像是在可见的航空彩色图像域中获得的。拟议的度量标准用于分别在这些真实和合成生成的图像中区分成对的真实和合成对象，例如冷却单元，工业建筑，房屋，输送机，烟囱，桩，铁路和池塘。所提出的方法成功地区分了航空彩色图像中的真实对象和合成对象，而无需任何先验知识或诸如光流之类的额外信息。我们根据这些指标的有效性对这些指标进行排名，以区分开销图像中的合成对象和真实对象。

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